Adaptive Allocation of Data-Objects in the Web Using Neural Networks

  • Joaquin Pérez O.
  • Rodolfo A. Pazos R.
  • Hector J. Fraire H.
  • Laura Cruz R.
  • Johnatan E. Pecero S.
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2829)


In this paper we address the problem of allocation scheme design of large database-objects in the Web environment, which may suffer significant changes in usage and access patterns and scaling of data. In these circumstances, if the design is not adjusted to new changes, the system can undergo severe degradations in data access costs and response time. Since this problem is NP-complete, obtaining optimal solutions for large problem instances requires applying approximate methods. We present a mathematical model to generate a new object allocation scheme and propose a new method to solve it. The method uses a Hopfield neural network with the mean field annealing (MFA) variant. The experimental results and a comparative study with other two methods are presented. The new method has a similar capacity to solve large problem instances, regular level of solution quality and excellent execution time with respect to other methods.


Tabu Search Reinforcement Learn Allocation Scheme Access Pattern Transmission Cost 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Joaquin Pérez O.
    • 1
  • Rodolfo A. Pazos R.
    • 1
  • Hector J. Fraire H.
    • 2
  • Laura Cruz R.
    • 2
  • Johnatan E. Pecero S.
    • 2
  1. 1.National Center of Research and Technology DevelopmentCuernavaca, Mor.México
  2. 2.Ciudad Madero Technology InstituteCd. Madero, Tam.México

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